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All generalization must by definition come from some form of abstraction. The abstractions that can be learned by DL models (often reflections of human abstractions injected via labels) are fairly weak and shallow, which is why DL is only capable of local generalization.
If AI today isn't capable of autonomous abstraction, it isn't because it's a particularly hard technical problem. Rather, it's a subtle conceptual problem. It doesn't seem like many people are looking at it. You can't produce the right answers if you're asking the wrong questions
AI today is to human intelligence what the phonograph was to human musicians. Superficially, it may seem like it has a comparable output & is ready to replace musicians (which was a real fear back then). But it has radically different capabilities: it's merely a recording device.
Don't confuse music creation with sound recording. Musicians are the origin of music. Unlike a phonograph, they can play a never-played before score, or write never-heard before music. Or take up a new instrument. Or invent a new musical genre.
Historically, we went technology to record sound to technology capable of helping with music production and creation. New, more powerful tools. Eventually, we will see open-ended autonomous music creation. This will be the trajectory of AI as well.
From recorded abstraction, to cognitive augmentation tools, to autonomous abstraction.
Can we have algorithms that evolves and learns autonomous abstraction by itself. Currently we fix the size and architecture of a neural net that we train. Can we have some algorithm that finds for itself the best size and architecture of a neural network.
That’s what Real AI when completed should look like,people right now are probably more focused on single components of what an AI will be.
Maybe AI should try solving hard and deep mathematical problems. Mathematics is the greatest example of human ability to create abstractions.
Is there a paper/book you would recommend to explore this line of thought?
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